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Clustering Multivariate Discrete Data with Partial Records

Abstract Number:

2116 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Kevin Giddings (1), Utkarsh Dang (2), Sanjeena Dang (3)

Institutions:

(1) N/A, N/A, (2) University of Guelph, N/A, (3) Carleton University, Ottawa, Canada

Co-Author(s):

Utkarsh Dang  
University of Guelph
Sanjeena Dang  
Carleton University

First Author:

Kevin Giddings  
N/A

Presenting Author:

Kevin Giddings  
N/A

Abstract Text:

Being able to cluster data with incomplete records is vital in many disciplines. Here, we develop a model-based clustering approach for clustering multivariate discrete data with missing entries using a mixture of multivariate Poisson lognormal distributions. A multivariate Poisson lognormal distribution is a hierarchical Poisson distribution that can account for over-dispersion and can model the correlation between the variables. To illustrate the effectiveness of this method, we have designed a variety of simulation studies to show the robustness of this new method under different percentages of incomplete records and patterns of missing data. Additionally, the approach is used to demonstrate clustering partial records from a proteomics dataset.

Keywords:

Clustering|Missing Data|Discrete Data|Multivariate Poisson Log Normal Distribution| |

Sponsors:

Biometrics Section

Tracks:

Missing Data

Can this be considered for alternate subtype?

Yes

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I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

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